Sparsity and Biomechanics Inspired Integration of Shape and Speckle Tracking for Cardiac Deformation Analysis
Cardiac motion analysis, particularly of the left ventricle (LV), can provide valuable information regarding the functional state of the heart. We propose a strategy of combining shape tracking and speckle tracking based displacements to calculate the dense deformation field of the myocardium.
We introduce the use and effects of \(l_1\) regularization, which induces sparsity, in our integration method. We also introduce regularization to make the dense fields more adhering to cardiac biomechanics. Finally, we motivate the necessity of temporal coherence in the dense fields and demonstrate a way of doing so.
We test our method on ultrasound (US) images acquired from six open-chested canine hearts. Baseline and post-occlusion strain results are presented for an animal, where we were able to detect significant change in the ischemic region. Six sets of strain results were also compared to strains obtained from tagged magnetic resonance (MR) data. Median correlation (with MR-tagging) coefficients of 0.73 and 0.82 were obtained for radial and circumferential strains respectively.
KeywordsEchocardiography Motion Shape tracking Speckle tracking Radial basis functions Regularization
Several members of Dr. Albert Sinusas’s lab, including, but not limited to, Christi Hawley, were involved in the image acquisitions. Members of Dr. Matthew O’Donnell’s lab developed the RF speckle tracking algorithms and code. Dr. Xiaojie Huang provided us the segmentation code. We would like to sincerely thank everyone for their contributions. This work was supported in part by the National Institute of Health (NIH) grant numbers R01HL121226 and T32HL098069.
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